Showing 11 results for Remote Sensing
A. Asgarian, B. Jabbarian Amiri, A. Alizadeh Shabani , J. Feghhi,
Volume 2, Issue 6 (3-2014)
Abstract
Constant urban development in today’s world has turned urban growth management into a main challenge in the 21st century. Obtaining spatiotemporal information about the pattern and rate of urban growth is critical to a better understanding of the urban growth process and practicing appropriate management policies. The present study investigated the trend of urban sprawl in Sari, Iran. First, land use and cover maps of the study area were prepared by processing multitemporal Landsat images from 1992, 2002, and 2010. Moreover, the urban growth of the city in 2010 was predicted by combining the Markov-cellular automata model with multi-criteria evaluation (overall Kappa = 83.80% area under the receiver operating characteristic curve ≈ 0.69). Afterward, the same model was applied to simulate urban development in 2021 and 2031. Built-up area per capita and Shannon entropy were then measured to assess urban sprawl in Sari. According to the results of change detection and simulation of urban growth of the study area, the built-up area had increased proportional to population growth since 1992. The same trend is expected to continue until 2031 when the urban area will exceed 2800 hectares. In addition, based on the relative values of Shannon entropy, although Sari has not yet faced the urban sprawl phenomenon, the absence of physical barriers around the city necessitates comprehensive urban management approaches to control urban sprawl and prevent future environmental problems.
S. Shamshiri, R. Jafari, S. Soltani, N. Ramezani,
Volume 3, Issue 8 (9-2014)
Abstract
The aim of this study was to investigate the status of dust storms in Kermanshah province, Iran, using climatic and remote sensing data. Dusty days recorded at 12 meteorological stations were obtained and analyzed at monthly and yearly scales from 1990 to 2011 and then the relationship of visibility data (<1000m) with other climatic parameters including temperature, relative humidity, rainfall, maximum wind speed and direction were evaluated using multivariable linear regression. Also, two important dust events on 15/9/2008 and 5/7/2009 were selected and then MODIS level 1B satellite data was converted to brightness temperature data with MODIS conversion Toolkit and then three main dust indices including Ackerman, Miller and TDI were used to map dust distribution in the study area. Results showed that the number of dusty days with a visibility less than 1000 m reached 71, 53 and 112 days in Kermanshah, Islamabadgharb and Sarepolezahab stations, respectively. The maximum dusty days (20 days/year) occurred in Somar station in the west of the Kermanshah province, which is the nearest station to Iraq. According to the regression analysis, the visibility data had the highest relationship (R2>0.77) with rainfall, wind direction and speed parameters. The results of MODIS dust mapping showed that the performance of dust indices differs from one event to another. According to dust classified maps and visibility data, the Ackerman index performed best, followed by TDI and Miller indices.
A. Najafi, M. H. Irannezhad, A. Sotoudeh, M. H. Mokhtari, B. Kiani,
Volume 4, Issue 14 (3-2016)
Abstract
Baghe-shadi in Yazd province is one the forests which is reported to be an area with high rate fire occurrence. The aim of this study was to model and map the fire risk area using geographic information system and remote sensing. In this study Analytical Hierarchy Process (AHP) with human related factors (distance from road, distance from settlements, and distance from vegetation), climatic related factors (air temperature and rainfall), and physiographic related factors (elevation, slope, aspect) were selected. Vegetation cover was estimated using Landsat OLI Normalized Difference Vegetation Index (NDVI). Weights were determined from specialists through questionnaire. Weight of each factor, elevation, slope and aspect, temperature, precipitation, distance from roads, distance from settlements and vegetation cover was achieved through multiple criteria decision making model, then areas with five susceptible classes were determined using GIS. Results showed that vegetation related factor and human related factor with weights of 0.569 and 0.204 are the most important factors respectively. In order to assess the accuracy of developed model, fire susceptibility map of was compared with the previously fired area. Result of comparison showed very high and high risk areas are corresponding to the controls area. Receiver operating charasteristic (ROC) test confirmed the high level (0.88) of accuracy of presented model.
N. Poorghasemi, M. Abbasi,
Volume 5, Issue 16 (9-2016)
Abstract
Leaf area index (LAI) is a key variable in primary production and carbon cycling in ecosystems. It is used as an important predictor to explain the processes of forest ecology, forest management, and remote sensing studies. Most of the remote sensing instruments such as LAI-2000 and Fisheye photography are based on three-dimensional space and they consider the geometry of the crown to estimate LAI. The aim of this study was to investigate the relationship between spectral behaviour of Quercus persica and Pistacia atlantica with two-dimensional and three-dimensional LAI. To estimate LAI, a box (0.5× 0.5× 0.5 meters) was placed in the four directions of the crown and all the leaves were harvested. In situ spectral measurements of leaves were done with ASD Fieldspec spectroradiometer. The results of partial least squares regression to model LAI form spectral data of Quercus persica showed maximum regression coefficient at visible and near infrared wavelengths for both LAI3D and LAI2D. The coefficient of determination (R2) between the measured and estimated LAI2D and LAI3D values for Quercus persica was 0.16 and 0.23 respectively, and for Pistacia atlantica was 0.15 and to 0.42, respectively. Generally, LAI3D showed better relationship with spectral reflectance for both species.
M. Royan, A. Sepehry, A. Salman Mahiny,
Volume 5, Issue 17 (12-2016)
Abstract
Remote sensing and aerial photography are means of exploring, studying and estimating vegetation variables such as species frequency and density in forests and rangelands. Common remote sensing images usually offer general information about vegetation parameters. For detailed information about vegetation (e.g. estimation of vegetation density and/or frequency), larger scale images are needed. The present research was conducted to estimate the density of rangeland vegetation in Inche Boroon area, north of Gorgan city. Using aerial photographs acquired from digital camera mounted on a tittered balloon in different flight altitudes, density and frequency of the main shrub species of the studied region, Halocnemum strobilaceum, were estimated on photographs at different scales (from 1:50 – to 1:1000) and were compared with field measurements. Results showed no significant difference between the field and image estimation of density below 1:600 (heights lower than 75 m) but at lower scales the difference was significant. No significant difference between field and image estimation of shrub frequency was also observed up to the scale of 1:1000. Due to the wider field of view of photographs at smaller scales, flight heights of 75 m and 130 m are thus suggested as the appropriate heights to estimate Halocnemum strobilaceum density and frequency respectively in the study area.
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Volume 6, Issue 4 (3-2018)
Abstract
Global warming and climate change due to increasing greenhouse gases (GHGs) concentration caused widespread concerns in the national and international societies. Carbon dioxide and methane as the most important greenhouse gases in the atmosphere account for more than about 80% of global warming due to greenhouse gases emission. In this study, Multivariate linear regression (method: enter and stepwise) was used to determine the relationship of CO2 and CH4 concentration with NDVI, LST, humidity, and temperature by GOSAT TANSO-FTS level 2 data, MODIS products (MOD13Q1 and MOD11C3), and meteorological parameters (Temperature and Humidity) in Iran. According to the results, the negative correlation among GHGs and NDVI, HUM, and HIG and the positive correlation among these gases and LST and TEM were observed in different seasons of 2013. These correlations showed concentration of carbon dioxide and methane diminished in the study area by increase in humidity, elevation and NDVI and decrease in LST. In spring, important factor in change of CO2 and CH4 was NDVI while, in other seasons climatic parameters were important.
R. Daneshmandparsa, R. Mirzaee, N. Bihamta,
Volume 7, Issue 2 (9-2018)
Abstract
Due to the importance of accessibility to updated and timely information regarding land cover changes, it is necessary for researchers and managers to assess such provincial level changes to help the planning process and prevent the damages caused in various regions. To this end, the Chaharmahal and Bakhtiari Province land cover changes from 2015-1994 were developed in six main classes using the hybrid method. Then land cover changes were determined by applying “after-classification comparison” and “landscape metric”. Therefore, MPS, LPI, NP and PLAND metrics were calculated at the class level, and SHDI, LPI, CONTAG SPILIT INDEX metrics were calculated to quantify the landscape patterns at the landscape level. Finally, for each land use type, the destruction rates and the human destruction index were calculated separately. The results indicated a sharp decline of %36.67 in pastures and 6.42% in the forests areas, as well as an increase 39.32% in the barren lands. In such a manner, the landscape is more fragmented, disordered (or unsystematic) and discontinuous plus it has become more diverse for the studied time period coverage. So, if the current trend continues, the a sharp decrease in the ecosystem services and functions is likely to occur.
M. Araghi Shahri, S. Soltani, M. Tarkesh, S. Pourmanafi,
Volume 9, Issue 3 (11-2020)
Abstract
One of the main scientific topics on the effects of global climate change is to assess changes in the carbon cycle in rangelands. Net Primary Production (NPP) is an important component of this cycle, in terms of carbon storage, and a key indicator for assessing the ecosystem function. This research aimed to investigate the correlation between NPP and ocean-atmospheric oscillations, monthly and seasonally, from 2000 to 2016 in the north of Iran’s Alborz Mountains. Net Primary Production of terrestrial vegetation was extracted from MODIS data and used in a model along with ocean-atmospheric oscillations. Multivariate regression analysis was used to investigate the simultaneous and lagged status in different timescales. Mann-Kendal test was used for trend analysis in different seasons over the studied period. Results showed that the highest NPP values were 2.06 and 1.30 g C m-2 d-1 in spring and summer and the lowest were 0.68 and 0.55 g C m-2 d-1 in autumn and winter, respectively. The trend of NPP variations was significantly different in autumn and winter. Overall, it was showed that NPP was affected by climatic variables, especially precipitation, and variables related to ENSO indicator are the main factors affecting precipitation, thereby affecting NPP in the north of Iran.
N. Divsalar, Seyyed M. Hashemi, S. Karbalay Saleh,
Volume 11, Issue 3 (12-2022)
Abstract
Remote sensing data play an important role in environmental planning and monitoring. The current study aimed to investigate the land surface temperature (LST) and the effect of environmental factors on the LST, to identify the temporal-spatial patterns and determine the hot spots in the period of 2013 to 2019, using Landsat 8 images. The effect of spectral indices: Normalized Difference Build-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on the surface temperature was investigated. Results indicated that the lowest average temperature has occurred in 2019 and the highest LST was in the 2017. The results of Moran's index correlation also showed that the most clustering pattern of LST, with the Moran value of 0.85 was obtained in 2019, the highest correlation between LST and NDBI, with the R value of 0.76 in the 2015, the highest correlation between LST and NDVI in the 2015 (R = -0.56), and the highest correlation between LST and NDWI in 2013 (R = -0.53). Rasht watershed in Guilan province is affected by human factors and land use changes. Therefore, it is recommended to increase the vegetation cover in urban areas, reduce the change of pasture to agricultural area, and reduce forest destruction.
F. Jafari, R. Jafari, H. Bashari,
Volume 11, Issue 4 (1-2023)
Abstract
Spatiotemporal changes of net primary production (NPP) is one of the essential indicators in determining rangeland ecosystem condition. Therefore, the aim of current research was to map and monitor the actual and potential NPP of rangeland ecosystems in Kohgiluyeh and Boyer-Ahmad province, using CASA and Miami models, MODIS and field data from 2009 to 2018. The accuracy of the produced NPP maps was assessed in 253 sampling sites located in different vegetation types with good, fair and poor rangeland conditions, using linear regression. Results showed that the difference between actual and potential NPP was greater than 56 gr C/m2/month, which can be a sign of human impacts and interferences in the rangeland ecosystems of the region. The highest and lowest relationships between modeled and field productions were observed in the Astragalus spp. - Bromus tomentellus vegetation type with good rangeland condition (R2=0.84) and Astragalus sieberi- Stipa capensis vegetation type with poor rangeland condition (R2=28), respectively. The present research findings indicated the importance of drought conditions, vegetation type and rangeland condition in estimating the actual and potential NPP and the difference of these productions can be used as an index to determine and monitor the condition of rangeland ecosystems.
M. R. Abolmaali, M. Tarkesh, A. Mousavi, H. R. Karimzadeh, S. Pourmanafi, S. Fakheran,
Volume 12, Issue 1 (5-2023)
Abstract
In order to make decisions for regional planning and achieve sustainable development, it is necessary to quantify land use and land cover changes. In this study, the land use and land cover maps of the Zayandehrood Dam watershed were prepared for the period of 1991 to 2021, using Landsat satellite images, and the changes that occurred in this period were revealed. Using the land change modeler (LCM), land use and land cover and their future changes for 2051 were modeled and predicted. The results showed that in the period between 1991 and 2021, the coverage of poor rangelands with 51,871 hectares of change had the largest decreasing trend, and the agricultural class had the largest increasing trend with 71,478 hectares of change. The largest decline in the period from 2021 to 2051 is related in the coverage of the fair rangelands class with 66192 hectares, and the agricultural class potentially has the largest increasing trend with 70328 hectares of change. The findings of this research will be useful for policymakers and planners. They can use the findings of this study for spatial planning in the region, managing the process of land use and land cover changes for sustainable development.
